# Bottom-up Broadcast Neural Network For Music Genre Classification

arXiv: https://arxiv.org/abs/1901.08928 · HTML: https://mlsanity.com/paper/1901.08928/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 10 of 10)

- [Joint Audio and Speech Understanding](https://mlsanity.com/paper/2309.14405/) (2023)
- [Layer Collaboration in the Forward-Forward Algorithm](https://mlsanity.com/paper/2305.12393/) (2023)
- [Multi-view Audio and Music Classification](https://mlsanity.com/paper/2103.02420/) (2021)
- [Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block](https://mlsanity.com/paper/2211.02940/) (2022)
- [MS-SincResNet: Joint learning of 1D and 2D kernels using multi-scale SincNet and ResNet for music genre classification](https://mlsanity.com/paper/2109.08910/) (2021)
- [A Study on Broadcast Networks for Music Genre Classification](https://mlsanity.com/paper/2208.12086/) (2022)
- [Content filtering methods for music recommendation: A review](https://mlsanity.com/paper/2507.02282/) (2025)
- [MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification](https://mlsanity.com/paper/2209.04109/) (2022)
- [Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms](https://mlsanity.com/paper/2307.10773/) (2023)
- [Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems](https://mlsanity.com/paper/2606.13858/) (2026)

## Abstract (arXiv)

Music genre recognition based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task. However, most of existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification. Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audios and develop a novel CNN architecture in this paper. The proposed CNN architecture takes the long contextual information into considerations, which transfers more suitable information for the decision-making layer. Various experiments on several benchmark datasets, including GTZAN, Ballroom, and Extended Ballroom, have verified the excellent performances of the proposed neural network. Codes and model will be available at "ttps://github.com/CaifengLiu/music-genre-classification".